[{"external_id":{"arxiv":["2412.04245"]},"OA_type":"green","publication":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops","day":"15","language":[{"iso":"eng"}],"department":[{"_id":"ChLa"}],"date_published":"2025-06-15T00:00:00Z","publication_identifier":{"isbn":["9798331599942"],"eissn":["2160-7516"],"issn":["2160-7508"]},"oa_version":"Preprint","scopus_import":"1","article_processing_charge":"No","conference":{"name":"CVPR: Conference on Computer Vision and Pattern Recognition","end_date":"2025-06-12","location":"Nashville, TN, United States","start_date":"2025-06-11"},"date_updated":"2025-10-13T07:18:26Z","oa":1,"arxiv":1,"citation":{"apa":"Prach, B., &#38; Lampert, C. (2025). Intriguing properties of robust classification. In <i>2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</i> (pp. 660–669). Nashville, TN, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPRW67362.2025.00071\">https://doi.org/10.1109/CVPRW67362.2025.00071</a>","chicago":"Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.” In <i>2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</i>, 660–69. IEEE, 2025. <a href=\"https://doi.org/10.1109/CVPRW67362.2025.00071\">https://doi.org/10.1109/CVPRW67362.2025.00071</a>.","mla":"Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.” <i>2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</i>, IEEE, 2025, pp. 660–69, doi:<a href=\"https://doi.org/10.1109/CVPRW67362.2025.00071\">10.1109/CVPRW67362.2025.00071</a>.","ieee":"B. Prach and C. Lampert, “Intriguing properties of robust classification,” in <i>2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</i>, Nashville, TN, United States, 2025, pp. 660–669.","ama":"Prach B, Lampert C. Intriguing properties of robust classification. In: <i>2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops</i>. IEEE; 2025:660-669. doi:<a href=\"https://doi.org/10.1109/CVPRW67362.2025.00071\">10.1109/CVPRW67362.2025.00071</a>","ista":"Prach B, Lampert C. 2025. Intriguing properties of robust classification. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. CVPR: Conference on Computer Vision and Pattern Recognition, 660–669.","short":"B. Prach, C. Lampert, in:, 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2025, pp. 660–669."},"type":"conference","date_created":"2025-10-12T22:01:26Z","abstract":[{"lang":"eng","text":"Despite extensive research since the community learned about adversarial examples 10 years ago, we still do not know how to train high-accuracy classifiers that are guaranteed to be robust to small perturbations of their inputs. Previous works often argued that this might be because no classifier exists that is robust and accurate at the same time. However, in computer vision this assumption does not match reality where humans are usually accurate and robust on most tasks of interest. We offer an alternative explanation and show that in certain settings robust generalization is only possible with unrealistically large amounts of data. Specifically, we find a setting where a robust classifier exists, it is easy to learn an accurate classifier, yet it requires an exponential amount of data to learn a robust classifier. Based on this theoretical result, we evaluate the influence of the amount of training data on datasets such as CIFAR10. Our findings indicate that the the amount of training data is the main factor determining the robust performance. Furthermore we show that that there are low magnitude directions in the data which are useful for non-robust generalization but are not available for robust classifiers. This implies that robust classification is a strictly harder tasks than normal classification, thereby providing an explanation why robust classification requires more data."}],"publication_status":"published","quality_controlled":"1","_id":"20455","related_material":{"record":[{"id":"18874","relation":"earlier_version","status":"public"}]},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2412.04245","open_access":"1"}],"status":"public","OA_place":"repository","month":"06","author":[{"first_name":"Bernd","id":"2D561D42-C427-11E9-89B4-9C1AE6697425","full_name":"Prach, Bernd","last_name":"Prach"},{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert"}],"publisher":"IEEE","doi":"10.1109/CVPRW67362.2025.00071","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"660-669","corr_author":"1","title":"Intriguing properties of robust classification","year":"2025"},{"quality_controlled":"1","publication_status":"published","publication":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","_id":"18248","day":"28","language":[{"iso":"eng"}],"status":"public","month":"07","date_published":"2020-07-28T00:00:00Z","author":[{"first_name":"Elad","full_name":"Amrani, Elad","last_name":"Amrani"},{"last_name":"Ben-Ari","first_name":"Rami","full_name":"Ben-Ari, Rami"},{"full_name":"Shapira, Inbar","first_name":"Inbar","last_name":"Shapira"},{"last_name":"Hakim","first_name":"Tal","full_name":"Hakim, Tal"},{"last_name":"Bronstein","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","orcid":"0000-0001-9699-8730"}],"publication_identifier":{"eissn":["2160-7516"],"isbn":["9781728193618"]},"publisher":"IEEE","doi":"10.1109/cvprw50498.2020.00485","scopus_import":"1","oa_version":"None","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","conference":{"name":"IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops","start_date":"2020-06-14","location":"Seattle, WA, United States","end_date":"2020-06-19"},"extern":"1","article_number":"9150938","date_updated":"2024-12-12T09:59:41Z","citation":{"short":"E. Amrani, R. Ben-Ari, I. Shapira, T. Hakim, A.M. Bronstein, in:, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2020.","ista":"Amrani E, Ben-Ari R, Shapira I, Hakim T, Bronstein AM. 2020. Self-supervised object detection and retrieval using unlabeled videos. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 9150938.","ama":"Amrani E, Ben-Ari R, Shapira I, Hakim T, Bronstein AM. Self-supervised object detection and retrieval using unlabeled videos. In: <i>2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/cvprw50498.2020.00485\">10.1109/cvprw50498.2020.00485</a>","ieee":"E. Amrani, R. Ben-Ari, I. Shapira, T. Hakim, and A. M. Bronstein, “Self-supervised object detection and retrieval using unlabeled videos,” in <i>2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</i>, Seattle, WA, United States, 2020.","mla":"Amrani, Elad, et al. “Self-Supervised Object Detection and Retrieval Using Unlabeled Videos.” <i>2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</i>, 9150938, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/cvprw50498.2020.00485\">10.1109/cvprw50498.2020.00485</a>.","chicago":"Amrani, Elad, Rami Ben-Ari, Inbar Shapira, Tal Hakim, and Alex M. Bronstein. “Self-Supervised Object Detection and Retrieval Using Unlabeled Videos.” In <i>2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/cvprw50498.2020.00485\">https://doi.org/10.1109/cvprw50498.2020.00485</a>.","apa":"Amrani, E., Ben-Ari, R., Shapira, I., Hakim, T., &#38; Bronstein, A. M. (2020). Self-supervised object detection and retrieval using unlabeled videos. In <i>2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</i>. Seattle, WA, United States: IEEE. <a href=\"https://doi.org/10.1109/cvprw50498.2020.00485\">https://doi.org/10.1109/cvprw50498.2020.00485</a>"},"year":"2020","title":"Self-supervised object detection and retrieval using unlabeled videos","type":"conference","date_created":"2024-10-08T13:05:08Z","abstract":[{"text":"Learning an object detection or retrieval system requires a large data set with manual annotations. Such data are expensive and time-consuming to create and therefore difficult to obtain on a large scale. In this work, we propose using the natural correlation in narrations and the visual presence of objects in video to learn an object detector and retriever without any manual labeling involved. We pose the problem as weakly supervised learning with noisy labels, and propose a novel object detection and retrieval paradigm under these constraints. We handle the background rejection by using contrastive samples and confront the high level of label noise with a new clustering score. Our evaluation is based on a set of ten objects with manual ground truth annotation in almost 5000 frames extracted from instructional videos from the web. We demonstrate superior results compared to state-of-the-art weakly- supervised approaches and report a strongly-labeled upper bound as well. While the focus of the paper is object detection and retrieval, the proposed methodology can be applied to a broader range of noisy weakly-supervised problems.","lang":"eng"}]}]